Abstract

Dynamic behavior of a new class of information-processing
systems called Cellular Neural Networks is investigated. In this paper we
introduce a small parameter in the state equation of a cellular neural
network and we seek for periodic phenomena. New approach is used for
proving stability of a cellular neural network by constructing Lyapunov's
majorizing equations. This algorithm is helpful for finding a map from
initial continuous state space of a cellular neural network into discrete
output. A comparison between cellular neural networks and cellular
automata is made.